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GongRzhe

JSON MCP Server

by GongRzhe
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation3/5

    The tools 'filter' and 'query' both operate on JSON data, which creates some overlap in purpose, as filtering can be seen as a subset of querying. However, the descriptions differentiate them: 'filter' uses conditions (likely simpler, boolean-based operations), while 'query' uses JSONPath syntax (a more expressive, path-based language). This distinction helps reduce confusion, but an agent might still struggle to choose between them for certain tasks.

    Naming Consistency5/5

    Both tool names follow a consistent pattern: they are single, lowercase verbs ('filter' and 'query') that clearly indicate actions. There are no deviations in style (e.g., no mixing with snake_case or camelCase), making the naming predictable and easy to understand across the set.

    Tool Count2/5

    With only 2 tools, this server feels thin for a JSON processing domain, which typically involves operations like parsing, transforming, validating, or merging JSON. The limited scope may force agents to work around gaps, as basic tasks like reading or writing JSON files are not covered, making it under-scoped for a general-purpose JSON server.

    Completeness2/5

    The tool set is severely incomplete for JSON processing. While 'filter' and 'query' handle retrieval and selection, there are no tools for creating, updating, validating, or manipulating JSON structures (e.g., add, remove, merge). This leaves significant gaps that will likely cause agent failures when trying to perform common JSON operations beyond simple queries.

  • Average 2.8/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Filter' implies a read-only operation, the description doesn't clarify whether this tool fetches data from a URL, processes it locally, or has any side effects like caching. It also omits details about error handling, performance, or output format, leaving significant gaps in understanding the tool's behavior.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence with no wasted words, making it appropriately concise. However, it lacks front-loading of critical information—it doesn't immediately clarify the tool's scope or differentiate it from siblings, which slightly reduces its effectiveness despite the brevity.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity of a tool that filters JSON data from a URL with three required parameters and no output schema, the description is incomplete. It fails to explain the output format, error conditions, or how the filtering integrates with the data source. Without annotations or an output schema, the agent lacks sufficient context to use the tool effectively.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, meaning the input schema already documents all three parameters with descriptions. The tool description adds no additional meaning beyond what the schema provides, such as explaining how parameters interact or providing usage examples. However, since the schema is comprehensive, the baseline score of 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Filter JSON data using conditions' states the tool's purpose with a clear verb ('Filter') and resource ('JSON data'), but it's vague about scope and doesn't distinguish from its sibling 'query'. It doesn't specify what kind of filtering or what the output looks like, leaving the agent uncertain about the exact operation.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus its sibling 'query', nor does it mention any prerequisites or alternative scenarios. Without any context about when this tool is appropriate, the agent must guess based on the tool name alone.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the full burden. It states the tool queries JSON data but doesn't disclose behavioral traits like error handling, performance implications, rate limits, or what happens if the URL is invalid or JSONPath is malformed. For a tool with no annotations, this is a significant gap in transparency.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence: 'Query JSON data using JSONPath syntax.' It is front-loaded with the core purpose and has zero waste, making it highly concise and well-structured.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity of querying external JSON data, the description is incomplete. No annotations exist, and there's no output schema, so the agent lacks information on return values, error cases, or behavioral constraints. The description doesn't compensate for these gaps, making it inadequate for safe and effective use.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, with clear descriptions for both parameters ('jsonPath' and 'url'). The description adds minimal value beyond the schema, as it only reiterates the use of JSONPath without providing additional syntax or format details. Baseline 3 is appropriate when the schema does the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Query JSON data using JSONPath syntax.' It specifies the verb ('query'), resource ('JSON data'), and method ('JSONPath syntax'). However, it doesn't explicitly differentiate from the sibling tool 'filter,' which might have overlapping functionality, preventing a score of 5.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'filter' or any other context for usage, such as prerequisites or scenarios where this tool is preferred. This leaves the agent with minimal direction.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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